Comparative Study of Extreme Learning Machine using Various Computing Platforms
Andrei Nour, Ioana Dogaru, Radu Dogaru · 2019
This paper focuses on testing the performance of an optimized extreme learning machine (ELM) algorithm using various resources-constrained computational platforms, to be used in autonomous intelligent vehicles. The purpose of this comparative study is to find the best suited embedded computational platform for future use in an experimental autonomous mobile robot, where computing power, responsiveness and power consumption are key to further development. The aforementioned algorithm was used as a benchmarking tool in this performance review, but also as an important starting point for processing large amounts of different data types from the robots' sensorial units. For now, the conducted experiments are limited to pre-trained datasets, to have a precise output of the very same data type.